IEEE Organizations related to Image Synthesis

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Conferences related to Image Synthesis

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2020 42nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC)

The conference program will consist of plenary lectures, symposia, workshops and invitedsessions of the latest significant findings and developments in all the major fields of biomedical engineering.Submitted papers will be peer reviewed. Accepted high quality papers will be presented in oral and postersessions, will appear in the Conference Proceedings and will be indexed in PubMed/MEDLINE


2020 IEEE 17th International Symposium on Biomedical Imaging (ISBI 2020)

The IEEE International Symposium on Biomedical Imaging (ISBI) is the premier forum for the presentation of technological advances in theoretical and applied biomedical imaging. ISBI 2020 will be the 17th meeting in this series. The previous meetings have played a leading role in facilitating interaction between researchers in medical and biological imaging. The 2020 meeting will continue this tradition of fostering cross-fertilization among different imaging communities and contributing to an integrative approach to biomedical imaging across all scales of observation.

  • 2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI)

    The IEEE International Symposium on Biomedical Imaging (ISBI) is the premier forum for the presentation of technological advances in theoretical and applied biomedical imaging.ISBI 2019 will be the 16th meeting in this series. The previous meetings have played a leading role in facilitating interaction between researchers in medical and biological imaging. The 2019 meeting will continue this tradition of fostering cross fertilization among different imaging communities and contributing to an integrative approach to biomedical imaging across all scales of observation.

  • 2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018)

    The IEEE International Symposium on Biomedical Imaging (ISBI) is the premier forum for the presentation of technological advances in theoretical and applied biomedical imaging. ISBI 2018 will be the 15th meeting in this series. The previous meetings have played a leading role in facilitating interaction between researchers in medical and biological imaging. The 2018 meeting will continue this tradition of fostering crossfertilization among different imaging communities and contributing to an integrative approach to biomedical imaging across all scales of observation.

  • 2017 IEEE 14th International Symposium on Biomedical Imaging (ISBI 2017)

    The IEEE International Symposium on Biomedical Imaging (ISBI) is the premier forum for the presentation of technological advances in theoretical and applied biomedical imaging. ISBI 2017 will be the 14th meeting in this series. The previous meetings have played a leading role in facilitating interaction between researchers in medical and biological imaging. The 2017 meeting will continue this tradition of fostering crossfertilization among different imaging communities and contributing to an integrative approach to biomedical imaging across all scales of observation.

  • 2016 IEEE 13th International Symposium on Biomedical Imaging (ISBI 2016)

    The IEEE International Symposium on Biomedical Imaging (ISBI) is the premier forumfor the presentation of technological advances in theoretical and applied biomedical imaging. ISBI 2016 willbe the thirteenth meeting in this series. The previous meetings have played a leading role in facilitatinginteraction between researchers in medical and biological imaging. The 2016 meeting will continue thistradition of fostering crossfertilization among different imaging communities and contributing to an integrativeapproach to biomedical imaging across all scales of observation.

  • 2015 IEEE 12th International Symposium on Biomedical Imaging (ISBI 2015)

    The IEEE International Symposium on Biomedical Imaging (ISBI) is the premier forum for the presentation of technological advances in theoretical and applied biomedical imaging. ISBI 2015 will be the 12th meeting in this series. The previous meetings have played a leading role in facilitating interaction between researchers in medical and biological imaging. The 2014 meeting will continue this tradition of fostering crossfertilization among different imaging communities and contributing to an integrative approach to biomedical imaging across all scales of observation.

  • 2014 IEEE 11th International Symposium on Biomedical Imaging (ISBI 2014)

    The IEEE International Symposium on Biomedical Imaging (ISBI) is the premier forum for the presentation of technological advances in theoretical and applied biomedical imaging. ISBI 2014 will be the eleventh meeting in this series. The previous meetings have played a leading role in facilitating interaction between researchers in medical and biological imaging. The 2014 meeting will continue this tradition of fostering crossfertilization among different imaging communities and contributing to an integrative approach to biomedical imaging across all scales of observation.

  • 2013 IEEE 10th International Symposium on Biomedical Imaging (ISBI 2013)

    To serve the biological, biomedical, bioengineering, bioimaging and other technical communities through a quality program of presentations and papers on the foundation, application, development, and use of biomedical imaging.

  • 2012 IEEE 9th International Symposium on Biomedical Imaging (ISBI 2012)

    To serve the biological, biomedical, bioengineering, bioimaging, and other technical communities through a quality program of presentations and papers on the foundation, application, development, and use of biomedical imaging.

  • 2011 IEEE 8th International Symposium on Biomedical Imaging (ISBI 2011)

    To serve the biological, biomedical, bioengineering, bioimaging, and other technical communities through a quality program of presentations and papers on the foundation, application, development, and use of biomedical imaging.

  • 2010 IEEE 7th International Symposium on Biomedical Imaging (ISBI 2010)

    To serve the biological, biomedical, bioengineering, bioimaging, and other technical communities through a quality program of presentations and papers on the foundation, application, development, and use of biomedical imaging.

  • 2009 IEEE 6th International Symposium on Biomedical Imaging (ISBI 2009)

    Algorithmic, mathematical and computational aspects of biomedical imaging, from nano- to macroscale. Topics of interest include image formation and reconstruction, computational and statistical image processing and analysis, dynamic imaging, visualization, image quality assessment, and physical, biological and statistical modeling. Molecular, cellular, anatomical and functional imaging modalities and applications.

  • 2008 IEEE 5th International Symposium on Biomedical Imaging (ISBI 2008)

    Algorithmic, mathematical and computational aspects of biomedical imaging, from nano- to macroscale. Topics of interest include image formation and reconstruction, computational and statistical image processing and analysis, dynamic imaging, visualization, image quality assessment, and physical, biological and statistical modeling. Molecular, cellular, anatomical and functional imaging modalities and applications.

  • 2007 IEEE 4th International Symposium on Biomedical Imaging: Macro to Nano (ISBI 2007)

  • 2006 IEEE 3rd International Symposium on Biomedical Imaging: Macro to Nano (ISBI 2006)

  • 2004 2nd IEEE International Symposium on Biomedical Imaging: Macro to Nano (ISBI 2004)

  • 2002 1st IEEE International Symposium on Biomedical Imaging: Macro to Nano (ISBI 2002)


2020 IEEE International Conference on Image Processing (ICIP)

The International Conference on Image Processing (ICIP), sponsored by the IEEE SignalProcessing Society, is the premier forum for the presentation of technological advances andresearch results in the fields of theoretical, experimental, and applied image and videoprocessing. ICIP 2020, the 27th in the series that has been held annually since 1994, bringstogether leading engineers and scientists in image and video processing from around the world.


2020 IEEE International Conference on Multimedia and Expo (ICME)

Multimedia technologies, systems and applications for both research and development of communications, circuits and systems, computer, and signal processing communities.

  • 2019 IEEE International Conference on Multimedia and Expo (ICME)

    speech, audio, image, video, text and new sensor signal processingsignal processing for media integration3D imaging, visualization and animationvirtual reality and augmented realitymulti-modal multimedia computing systems and human-machine interactionmultimedia communications and networkingmedia content analysis and searchmultimedia quality assessmentmultimedia security and content protectionmultimedia applications and servicesmultimedia standards and related issues

  • 2018 IEEE International Conference on Multimedia and Expo (ICME)

    The IEEE International Conference on Multimedia & Expo (ICME) has been the flagship multimedia conference sponsored by four IEEE societies since 2000. It serves as a forum to promote the exchange of the latest advances in multimedia technologies, systems, and applications from both the research and development perspectives of the circuits and systems, communications, computer, and signal processing communities. ICME also features an Exposition of multimedia products and prototypes.

  • 2017 IEEE International Conference on Multimedia and Expo (ICME)

    Topics of interest include, but are not limited to: – Speech, audio, image, video, text and new sensor signal processing – Signal processing for media integration – 3D visualization and animation – 3D imaging and 3DTV – Virtual reality and augmented reality – Multi-modal multimedia computing systems and human-machine interaction – Multimedia communications and networking – Media content analysis – Multimedia quality assessment – Multimedia security and content protection – Multimedia databases and digital libraries – Multimedia applications and services – Multimedia standards and related issues

  • 2016 IEEE International Conference on Multimedia and Expo (ICME)

    Topics of interest include, but are not limited to:- Speech, audio, image, video, text and new sensor signal processing- Signal processing for media integration- 3D visualization and animation- 3D imaging and 3DTV- Virtual reality and augmented reality- Multi-modal multimedia computing systems and human-machine interaction- Multimedia communications and networking- Media content analysis- Multimedia quality assessment- Multimedia security and content protection- Multimedia databases and digital libraries- Multimedia applications and services- Multimedia standards and related issues

  • 2015 IEEE International Conference on Multimedia and Expo (ICME)

    With around 1000 submissions and 500 participants each year, the IEEE International Conference on Multimedia & Expo (ICME) has been the flagship multimedia conference sponsored by four IEEE societies since 2000. It serves as a forum to promote the exchange of the latest advances in multimedia technologies, systems, and applications from both the research and development perspectives of the circuits and systems, communications, computer, and signal processing communities.

  • 2014 IEEE International Conference on Multimedia and Expo (ICME)

    The IEEE International Conference on Multimedia & Expo (ICME) has been the flagship multimedia conference sponsored by four IEEE societies since 2000. It serves as a forum to promote the exchange of the latest advances in multimedia technologies, systems, and applications. In 2014, an Exposition of multimedia products, prototypes and animations will be held in conjunction with the conference.Topics of interest include, but are not limited to:

  • 2013 IEEE International Conference on Multimedia and Expo (ICME)

    To promote the exchange of the latest advances in multimedia technologies, systems, and applications from both the research and development perspectives of the circuits and systems, communications, computer, and signal processing communities.

  • 2012 IEEE International Conference on Multimedia and Expo (ICME)

    IEEE International Conference on Multimedia & Expo (ICME) has been the flagship multimedia conference sponsored by four IEEE Societies. It exchanges the latest advances in multimedia technologies, systems, and applications from both the research and development perspectives of the circuits and systems, communications, computer, and signal processing communities.

  • 2011 IEEE International Conference on Multimedia and Expo (ICME)

    Speech, audio, image, video, text processing Signal processing for media integration 3D visualization, animation and virtual reality Multi-modal multimedia computing systems and human-machine interaction Multimedia communications and networking Multimedia security and privacy Multimedia databases and digital libraries Multimedia applications and services Media content analysis and search Hardware and software for multimedia systems Multimedia standards and related issues Multimedia qu

  • 2010 IEEE International Conference on Multimedia and Expo (ICME)

    A flagship multimedia conference sponsored by four IEEE societies, ICME serves as a forum to promote the exchange of the latest advances in multimedia technologies, systems, and applications from both the research and development perspectives of the circuits and systems, communications, computer, and signal processing communities.

  • 2009 IEEE International Conference on Multimedia and Expo (ICME)

    IEEE International Conference on Multimedia & Expo is a major annual international conference with the objective of bringing together researchers, developers, and practitioners from academia and industry working in all areas of multimedia. ICME serves as a forum for the dissemination of state-of-the-art research, development, and implementations of multimedia systems, technologies and applications.

  • 2008 IEEE International Conference on Multimedia and Expo (ICME)

    IEEE International Conference on Multimedia & Expo is a major annual international conference with the objective of bringing together researchers, developers, and practitioners from academia and industry working in all areas of multimedia. ICME serves as a forum for the dissemination of state-of-the-art research, development, and implementations of multimedia systems, technologies and applications.

  • 2007 IEEE International Conference on Multimedia and Expo (ICME)

  • 2006 IEEE International Conference on Multimedia and Expo (ICME)

  • 2005 IEEE International Conference on Multimedia and Expo (ICME)

  • 2004 IEEE International Conference on Multimedia and Expo (ICME)

  • 2003 IEEE International Conference on Multimedia and Expo (ICME)

  • 2002 IEEE International Conference on Multimedia and Expo (ICME)

  • 2001 IEEE International Conference on Multimedia and Expo (ICME)

  • 2000 IEEE International Conference on Multimedia and Expo (ICME)


ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

The ICASSP meeting is the world's largest and most comprehensive technical conference focused on signal processing and its applications. The conference will feature world-class speakers, tutorials, exhibits, and over 50 lecture and poster sessions.


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Periodicals related to Image Synthesis

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Most published Xplore authors for Image Synthesis

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Xplore Articles related to Image Synthesis

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Comparison of Deep Generative Models for the Generation of Handwritten Character Images

2019 27th Signal Processing and Communications Applications Conference (SIU), 2019

In this study, we compare deep learning methods for generating images of handwritten characters. This problem can be thought of as a restricted Turing test: A human draws a character from any desired alphabet and the system synthesizes images with similar appearances. The intention here is not to merely duplicate the input image but to add random perturbations to give ...


Fire Image Generation Based on ACGAN

2019 Chinese Control And Decision Conference (CCDC), 2019

In order to solve the problem that it is difficult to obtain fire image data in CNN training, this paper discusses the method of generating fire image by means of generative adversarial networks. How to generate the desired fire image according to the known observation variables is discussed. According to the structure of InfoGAN and ACGAN, a GAN structure for ...


A Camera Simulation Framework for Passive Depth Recovery Systems

IEEE Photonics Journal, 2010

This paper presents a novel camera simulation framework capable of simulating the optical path of a variety of camera systems through the technique of Monte Carlo Path tracing. Path tracer is a ray-tracing technique that uses Markov chains to solve the global illumination problem, i.e., the problem of calculating the distribution of light in an environment, taking into account all ...


Hierarchically-Fused Generative Adversarial Network for Text to Realistic Image Synthesis

2019 16th Conference on Computer and Robot Vision (CRV), 2019

In this paper, we present a novel Hierarchically-fused Generative Adversarial Network (HfGAN) for synthesizing realistic images from text descriptions. While existing approaches on this topic have achieved impressive success, to generate 256×256 images from captions, they commonly resort to coarse-to-fine scheme and associate multiple discriminators in different stages of the networks. Such a strategy is both inefficient and prone to ...


Part-Preserving Pose Manipulation for Person Image Synthesis

2019 IEEE International Conference on Multimedia and Expo (ICME), 2019

Manipulating person images under diverse poses, which transfers a person from one pose to another desired pose, is an interesting yet challenging task due to large non-rigid spatial deformation. Most existing works fail to preserve the fine-grained appearance consistency along with the pose changes due to the lack of explicit constraints and spatial modeling, leading to unrealistic results with severe ...


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Educational Resources on Image Synthesis

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IEEE-USA E-Books

  • Comparison of Deep Generative Models for the Generation of Handwritten Character Images

    In this study, we compare deep learning methods for generating images of handwritten characters. This problem can be thought of as a restricted Turing test: A human draws a character from any desired alphabet and the system synthesizes images with similar appearances. The intention here is not to merely duplicate the input image but to add random perturbations to give the impression of being human-produced. For this purpose, the images produced by two different generative models (Generative Adversarial Network and Variational Autoencoder) and the related training method (Reptile) are examined with respect to their visual quality in a subjective manner. Also, the capability of transferring the knowledge that is obtained by the model is challenged by using different datasets for the training and test processes. Using the proposed model and meta-learning method, it is possible to produce not only images similar to the ones in the training set but also novel images that belong to a class which is seen for the first time.

  • Fire Image Generation Based on ACGAN

    In order to solve the problem that it is difficult to obtain fire image data in CNN training, this paper discusses the method of generating fire image by means of generative adversarial networks. How to generate the desired fire image according to the known observation variables is discussed. According to the structure of InfoGAN and ACGAN, a GAN structure for generating fire image is proposed. Fire area is selected as a known observation variable to generate the corresponding fire image. Experiments show that the network structure can generate the required images according to the values of a observed variables. And the quality of the generated image is related to the distribution of observed variables in the data set.

  • A Camera Simulation Framework for Passive Depth Recovery Systems

    This paper presents a novel camera simulation framework capable of simulating the optical path of a variety of camera systems through the technique of Monte Carlo Path tracing. Path tracer is a ray-tracing technique that uses Markov chains to solve the global illumination problem, i.e., the problem of calculating the distribution of light in an environment, taking into account all forms of scattering, absorption, and interreflection. In global illumination, we deal with the interaction of light that reaches a surface directly from a light source (direct lighting) as well as the interaction of light that reaches a surface as a result of scattering or transmission from or through other objects (indirect lighting). Available pieces of ray-tracer software use very simple models for their camera system like the pinhole camera, the thin-lens camera, and the thick-lens camera. The novelty and strength of our simulation tool is the capability to simulate any arbitrary and complex camera system. Any kind of optical component (like mirrors, prisms, and optical filters) can be placed inside the camera system or on the image sensor, and the tool synthesizes the image taken by that complex camera system, which can be used to optimize the parameters of the system for a specific application. The tool was used to simulate the optical path of a variety of passive depth recovery systems (like stereoscopy, Plenoptic Camera, and Bi-prism camera) that are included in this paper.

  • Hierarchically-Fused Generative Adversarial Network for Text to Realistic Image Synthesis

    In this paper, we present a novel Hierarchically-fused Generative Adversarial Network (HfGAN) for synthesizing realistic images from text descriptions. While existing approaches on this topic have achieved impressive success, to generate 256×256 images from captions, they commonly resort to coarse-to-fine scheme and associate multiple discriminators in different stages of the networks. Such a strategy is both inefficient and prone to artifacts. Motivated by the above findings, we propose an end-to-end network that can generate 256×256 photo-realistic images with only one discriminator. We fully exploit the hierarchical information from different layers and directly generate the fine-scale images by adaptively fusing features from multi- hierarchical layers. We quantitatively evaluate the synthesized images with Inception Score, Visual-semantic Similarity and average training time on the CUB birds, Oxford-102 flowers, and COCO datasets. The results show that our model is more efficient and noticeably outperforms the previous state-of-the- art methods.

  • Part-Preserving Pose Manipulation for Person Image Synthesis

    Manipulating person images under diverse poses, which transfers a person from one pose to another desired pose, is an interesting yet challenging task due to large non-rigid spatial deformation. Most existing works fail to preserve the fine-grained appearance consistency along with the pose changes due to the lack of explicit constraints and spatial modeling, leading to unrealistic results with severe artifacts. In this paper, we propose a novel Part- Preserving Generative Adversarial Network (PP-GAN) to achieve good manipulation quality by explicitly enforcing rich structure constraints over generative modeling. PP-GAN is proposed to decompose the challenging spatial transformation of the whole body into fine-grained part-level transformations, which are then integrated via human joint structure constraint. Given arbitrary poses, PP-GAN integrates human joint structure and region-level part cues as inputs to perform explicit generative modeling. Besides, we introduce a parsing-consistent loss to enforce semantic consistency among images with diverse poses, which guides the image synthesis from a semantic perspective. Extensive qualitative and quantitative evaluations on two benchmarks show that our PP-GAN significantly outperforms the state-of-the-art baselines in generating more realistic and plausible image synthesis results. PP-GAN successfully preserves part-level characteristics even for most challenging pose changes while prior works are easy to fail.

  • Style-Controlled Synthesis of Clothing Segments for Fashion Image Manipulation

    We propose an approach for digitally altering people's outfits in images. Given images of a person and a desired clothing style, our method generates a new clothing item image. The new item displays the color and pattern of the desired style while geometrically mimicking the person's original item. Through superimposition, the altered image is made to look as if the person is wearing the new item. Unlike recent works with full-image synthesis, our work relies on segment synthesis, yielding benefits in virtual try-on. For the synthesis process, we assume two underlying factors characterizing clothing segments: geometry and style. These two factors are disentangled via preprocessing and combined using a neural network. We explore several networks and introduce important aspects of the architecture and learning process. Our experimental results are three-fold: (1) on images from fashion-parsing datasets, we demonstrate the generation of high-quality clothing segments with fine-level style control; (2) on a virtual try-on benchmark, our method shows superiority over prior synthesis methods; (3) in transferring clothing styles, we visualize the differences between our method and neural style transfer.

  • Photographic Image Synthesis with Highway Residual U-net

    Photographic image synthesis is a new research focus in the field of deep learning, which uses the known image description information to generate the image approaching to the real scene. In this paper, a novel photographic image synthesis method based on the highway residual U-net (HRU) is proposed. The proposed highway residual blocks (HRBs) are embedded between the levels of the U-net and the proposed HRBs effectively filter the information transmitted by each level of the U-net and speed up the convergence of the network. In addition, the resize-convolution layers in HRU are used to replace the deconvolution layers to reduce the checkerboard artifacts in the synthesized images. HRU can be trained end-to-end with the image description map and the corresponding photographic image. Extensive experiments on Cityscapes dataset and GTA5 dataset demonstrate that images synthesized by the presented approach are considerably more realistic than other synthetic approaches.

  • Unsupervised Facial Image Synthesis Using Two-Discriminator Adversarial Autoencoder Network

    Recent years have witnessed the unprecedented success in single image synthesis by the means of convolutional neural networks (CNNs). High-level synthesis of facial image such as expression translation and attribute swap is still a challenging task due to high non-linearity. Previous methods suffer from the limitations that being unable to transfer multiple face attributes simultaneously, or incapability of transferring an attribute to another by a continuously changing way. To address this problem, we propose a two- discriminator adversarial autoencoder network (TAAN). The latent-discriminator is trained to disentangle an input image from its original facial attribute, while the pixel-discriminator is trained to make the output image attach to the target facial attribute. By controlling the attribute values, we can choose which and how much a specific attribute can be perceivable in the generated image. Quantitative and qualitative evaluations are conducted on the celebA and KDEF datasets, and the comparison with the state-of-the-art methods shows the competency of our proposed TAAN.

  • High-Resolution Driving Scene Synthesis Using Stacked Conditional Gans and Spectral Normalization

    Large-scale dataset plays a key role in the driving scene understanding for deep learning based-autonomous driving tasks. Due to the fact that the annotation for a large number of images is extremely labor-intensive and time- consuming, many researchers turn to using image-synthesis techniques for automatic construction of training data. However, traditional methods often have difficulties in producing high-definition driving scene images. To tackle this problem, in this paper, we propose a novel deep model - hdCGAN - for high-definition image-to-image translation. The hdCGAN is built on a conditional GAN in combination with a spectral normalization. Moreover, we improve the hdCGAN by using a stacked network architecture and the enhanced model is called stack-hdCGAN. With the guidance of multi-scale discriminators and the constraint of spectral normalization in the training procedure, the learned models can generate high-resolution and high-quality driving scene images from corresponding semantic segmentation maps. Quantitative and qualitative evaluations on the Cityscapes dataset demonstrate the effectiveness of the proposed models.

  • Semantic GAN: Application for Cross-Domain Image Style Transfer

    Image style transfer has attracted much attention from many fields and received promising performance. However, style transfer in the cross-domain field, e.g., the transfer between near-infrared and visible light images, is rarely studied. In the cross-domain image style transfer, one key issue is mismatching problem existing in the generated semantic regions. In this paper, we propose a novel model of Semantic GAN, which integrates the semantic guidance and the recent CycleGAN. In particular, we present a semantic style loss with Gram matrix to well preserve the semantic information in the generated images. The proposed Semantic GAN can control the transfer in the right way with semantic masks and solve the mismatching problem. We apply our approach to two outdoor scene datasets to evaluate the performance of all competing methods. The experimental results show that our approach outperforms previous methods in addressing the mismatching problem and providing a good quality result.



Standards related to Image Synthesis

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